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Related Experiment Video

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Deep-Learning Terahertz Single-Cell Metabolic Viability Study.

Ning Yang1, Qian Shi1, Mingji Wei1

  • 1School of Electrical Information Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China.

ACS Nano
|September 28, 2023
PubMed
Summary

This study introduces a novel cell viability assessment method using terahertz absorption spectroscopy. This label-free, contact-free technique accurately measures single-cell metabolic activity and apoptosis.

Keywords:
absorption spectrumcell apoptosiscell viabilitydeep learningmachine learningspectroscopyterahertz

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Area of Science:

  • Biophysics
  • Cell Biology
  • Spectroscopy

Background:

  • Accurate cell viability assessment is crucial for biological research and drug development.
  • Existing methods often lack precision or require complex sample preparation.

Purpose of the Study:

  • To develop a novel, accurate, and non-invasive method for assessing single-cell viability.
  • To correlate terahertz absorption with cellular metabolic state and apoptosis.

Main Methods:

  • Measuring single-cell absorption of terahertz laser beams without culture medium.
  • Utilizing a convolutional neural network for cell viability classification based on morphology.
  • Establishing a quantitative model relating terahertz absorbance to cell viability.

Main Results:

  • Terahertz absorption measurements accurately reflect single-cell metabolic activity.
  • Changes in terahertz absorbance correlate directly with the apoptosis process under stress.
  • A new definition of cell viability based on stable terahertz absorbance is proposed.

Conclusions:

  • The developed terahertz absorption spectroscopy method offers an accurate, label-free, and contact-free approach to cell viability assessment.
  • This technique can visualize the cell apoptosis process, enabling broad applications in drug screening and fundamental research.